Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Stat Med. 2013 Aug 15;32(18):3158-80. doi: 10.1002/sim.5732. Epub 2013 Jan 11.
The use of individual participant data (IPD) from multiple studies is an increasingly popular approach when developing a multivariable risk prediction model. Corresponding datasets, however, typically differ in important aspects, such as baseline risk. This has driven the adoption of meta-analytical approaches for appropriately dealing with heterogeneity between study populations. Although these approaches provide an averaged prediction model across all studies, little guidance exists about how to apply or validate this model to new individuals or study populations outside the derivation data. We consider several approaches to develop a multivariable logistic regression model from an IPD meta-analysis (IPD-MA) with potential between-study heterogeneity. We also propose strategies for choosing a valid model intercept for when the model is to be validated or applied to new individuals or study populations. These strategies can be implemented by the IPD-MA developers or future model validators. Finally, we show how model generalizability can be evaluated when external validation data are lacking using internal-external cross-validation and extend our framework to count and time-to-event data. In an empirical evaluation, our results show how stratified estimation allows study-specific model intercepts, which can then inform the intercept to be used when applying the model in practice, even to a population not represented by included studies. In summary, our framework allows the development (through stratified estimation), implementation in new individuals (through focused intercept choice), and evaluation (through internal-external validation) of a single, integrated prediction model from an IPD-MA in order to achieve improved model performance and generalizability.
当开发多变量风险预测模型时，使用来自多个研究的个体参与者数据（IPD）是一种越来越受欢迎的方法。然而，相应的数据集通常在重要方面存在差异，例如基线风险。这促使人们采用荟萃分析方法来适当处理研究人群之间的异质性。尽管这些方法提供了一个适用于所有研究的平均预测模型，但对于如何将该模型应用于新个体或原始数据以外的研究人群，或者如何验证该模型，几乎没有指导。我们考虑了几种方法，从具有潜在研究间异质性的 IPD 荟萃分析（IPD-MA）中开发多变量逻辑回归模型。我们还提出了在模型需要验证或应用于新个体或研究人群时选择有效模型截距的策略。这些策略可以由 IPD-MA 开发者或未来的模型验证者实施。最后，我们展示了当缺乏外部验证数据时如何使用内部-外部交叉验证来评估模型的可推广性，并将我们的框架扩展到计数和时间到事件数据。在实证评估中，我们的结果表明分层估计如何允许特定于研究的模型截距，然后可以告知在实际应用模型时使用的截距，即使对于未包含在研究中的人群也是如此。总之，我们的框架允许通过分层估计（通过分层估计）、在新个体中实施（通过集中的截距选择）以及通过内部-外部验证（通过内部-外部验证）来开发、实施和评估单个综合预测模型，从而提高模型性能和可推广性。
